One remedy to the problem is to sample challenging negative documents that have high ranking scores assigned by a sparse ranking model such as BM25 [1]}, [2]}.
The drawback of this approach is that the training signals provided by the sparse model helps improve the performance of the target dense retrieval model by forcing it to mimic the sparse model. Thus, the trained dense model is unlikely to suppress or compensate the sparse model [3]}.
